Graduate Program

MS Program

Our Master of Science in Data Science program is accredited by New York State and provides a strong background in the both the fundamentals and applications of data science.

The program can be completed in either two semesters (fall/spring) or three semesters (fall/spring/fall) of full-time study. The two semester version is appropriate for students who enter with a strong background in computer science and mathematics, and are eager to take on a relatively heavy course load (four courses per semester) in order to graduate quickly. In the three semester version, students take three courses per semester, and many students work at internships during the summer between the spring and fall semesters. Our program provides opportunities for students to meet corporate recruiters and provides advice on applying for internships, but we do not guarantee placement in an internship.

The program is designed for students with a background in any field of science, engineering, mathematics, or business. We welcome mid-career applicants as well as students fresh out of college. Prospective students should have experience in programming, and should be comfortable with first-year college mathematics.

The components of the program are as follows:

An optional summer bridging course for students who come without a strong computer science background.

Four required core courses for a total of 16 credits. Students may place out of one or more of the required core courses, but will still be required to complete the 30 credits required for the program.

A required 4 credit practicum in which the student works in a team to implement a significant system or analysis with a final oral presentation provided by each student. A committee of two faculty members from within the institute will evaluate the final oral presentation in order for it to serve as the master’s degree exit exam.

Three electives selected from the area courses or research, for a total of 10 credits or more. Some of the area courses have prerequisites that students must satisfy. Eight credits or more in one area would constitute a concentration, but a concentration is not required.

A total of 30 credits are required to complete the program (without the bridging course) and many students will finish the program with more than 30 credits, depending on the elective area courses they select.

Optional Summer Bridging Course

CSC 162: The Art of Data Structures*

*Students will be notified in their offer letter if they are required to take this course.

Practicum

Area Courses

A minimum of 10 credits total required, across three areas. Eight or more of these credits in one specific area will qualify as a concentration though a concentration is not necessary for graduation. Students have the option to substitute an independent study (DSC 491) in place of an area course with the appropriate permissions.

DSC 530: Methods in Data-Enabled Research into Human Behavior and its Cognitive and Neural Mechanisms (NRT students only) (fall)

DSC 531: Methods in Data-Enabled Research into Human Behavior and its Cognitive and Neural Mechanisms Practicum (NRT students only) (spring)

Business and Social Science*

CIS 417*: Introduction to Business Analytics (Fall-A, Spring-B)

CIS 418*: Advanced Business Modeling and Analytics (Fall-A, Spring-B)

CIS 432* Predictive Analytics/Python (Fall-B)

CIS 434*: Social Media Analytics (Spring-A)

CIS 442F*: Big Data (Spring-B)

FIN 418*: Quantitative Finance w/ Python (Fall-A)

MKT 412*: Marketing Research (Fall-B)

MRT 436R*: Marketing Analytics using R (Fall-A)

MKT 437*: Digital Marketing Strategy (Fall-B, Spring-A)

MKT 451*: Advanced Quant Marketing

PSC 404: Probability and Inference (fall)

PSC 405: Linear Models (spring)

PSC 504: Causal Inference (spring)

PSC 505: Maximum Likelihood Estimation (fall)

*Please note that any course in this concentration that is housed in the Simon Business School does not run on the full semester system and is offered at a different credit hour rate than AS&E courses.